Semi-autonomous and autonomous robots often may operate in environments that are considered “dynamic” because various attributes of the environment, such as locations of obstacles, locations of other robots, or other hazards (e.g., people), etc., may change. To cope with a changing environment, each robot should have the ability to perform its assigned task and yet have sufficient autonomy to also perform obstacle avoidance when necessary. A semi-autonomous or autonomous robot's computing resources, such as memory and processor cycles, may be heavily utilized to properly react to unforeseen obstacles and/or hazards in its own vicinity while still performing its assigned task. Thus, in a complex dynamic environment, it may be impracticable for each robot to have or be provided comprehensive, or even extensive, knowledge of changing environmental attributes. Accordingly, various techniques exist for performing high level (i.e. low resolution) robotic path planning at a global level, e.g., by a global path planner, and for performing low level (i.e. high resolution) robotic path planning at a local level, e.g., by a local path planner implemented on the robot itself. However, existing techniques may not take into account changing attributes of the robot environment that are detected by the global planner and/or desired “reactivity” of the robots.